The present invention relates generally to imaging systems, such as radiographic systems, and more particularly, to processing techniques for dual-energy radiography. Even more particularly, the present invention relates to a system and method for selecting decomposition parameters WS and WB for creating soft tissue and bone images from low and high-energy images acquired from an imaging system, such as a dual-energy digital radiography system using flat-panel technology.
Medical diagnostic and imaging systems are ubiquitous in modern health care facilities. Currently, a number of modalities exist for medical diagnostic and imaging systems. These include computed tomography (CT) systems, x-ray systems (including both conventional and digital or digitized imaging systems), magnetic resonance (MR) systems, positron emission tomography (PET) systems, ultrasound systems, nuclear medicine systems, and so forth. Such systems provide invaluable tools for identifying, diagnosing and treating physical conditions and greatly reduce the need for surgical diagnostic intervention. In many instances, these modalities complement one another and offer the physician a range of techniques for imaging particular types of tissue, organs, physiological systems, and so forth.
Digital imaging systems are becoming increasingly widespread for producing digital data that can be reconstructed into useful radiographic images. In one application of a digital imaging system, radiation from a source is directed toward a subject, typically a patient in a medical diagnostic application, and a portion of the radiation passes through the subject and impacts a detector. The surface of the detector converts the radiation to light photons, which are sensed. The detector is divided into an array of discrete picture elements or pixels, and encodes output signals based upon the quantity or intensity of the radiation impacting each pixel region. Because the radiation intensity is altered as the radiation passes through the subject, the images reconstructed based upon the output signals may provide a projection of tissues and other features similar to those available through conventional photographic film techniques. In use, the signals generated at the pixel locations of the detector are sampled and digitized. The digital values are transmitted to processing circuitry where they are filtered, scaled, and further processed to produce the image data set. The data set may then be used to reconstruct the resulting image, to display the image, such as on a computer monitor, to transfer the image to conventional photographic film, and so forth.
In dual-energy imaging systems, such as dual-energy digital radiography systems, the system acquires two images of a desired anatomical region of a patient at different energy levels, such as low and high energy levels. The two images are then used to decompose the anatomy and to create soft tissue and bone images of the desired anatomical region. The two images are generally decomposed according to the dual-energy decomposition equations:IS=IH/ILWSIB=IH/ILWBwhere IS represents the soft tissue image, IB represents the bone image, IH represents the high-energy image, IL represents the low-energy image, WS is the soft tissue decomposition parameter, WB is the bone decomposition parameter, and 0<WS<WB<1. The soft tissue and bone decomposition parameters must be selected carefully to provide acceptable dual-energy image quality. Unfortunately, the soft tissue and bone decomposition parameters may be functions of several image and techniques variables, thereby complicating the selection of these parameters. Moreover, the decomposed images typically have significant noise, contrast artifacts, and motion artifacts, which degrade the images and reduce the value of the images for medical diagnosis. These artifacts are generally mitigated by post-decomposition processing techniques, yet the decomposed images still exhibit significant artifacts.
At relatively attenuated regions of the image, the foregoing dual-energy decomposition equations produce relatively noisy decomposed images. For example, during a low-dose clinical data acquisition, the computationally efficient decomposition equations amplify noise and produce very noisy decomposed images at highly attenuated regions of the image. Existing noise reduction techniques mitigate noise in the images after decomposition by the foregoing decomposition equations. However, the foregoing decomposition equations tend to amplify noise in the images, and the existing noise reduction techniques fail to mitigate the noise adequately.
Artifacts also may arise in the decomposed images due to anatomical movement between the two image acquisitions. Although the two images may be acquired over a relatively short time interval, such as 100–200 ms, these motion artifacts may significantly degrade the quality of the decomposed images. For chest radiography, the motion artifacts manifest as residual rib contrast, which causes rib structure to be visible in the soft tissue image. The residual rib structure, which is present in about 30 percent of acquisitions, decreases the conspicuity of lung pathology and essentially defeats the purpose of generating soft tissue lung images by dual-energy imaging. Traditional methods to correct for motion artifacts are relatively ineffective for dual-energy imaging, because the dual-energy images have significantly different local contrasts.
Accordingly, a technique is needed for reducing noise, contrast, and motion artifacts in the images decomposed from a dual-energy imaging system, such as a dual-energy digital radiography imaging system. A technique is also needed for selecting parameters for the dual-energy decomposition process. It also would be advantageous to automate various aspects of the image processing and decomposition process, including the selection of decomposition parameters.